A Cognitive Approach for Reproducing the Homing Behaviour of

Honey Bees

Xin Yuan, Michael John Liebelt and Braden J. Phillips

The School of Electrical and Electronic Engineering, The University of Adelaide, Adelaide, South Australia, Australia

Keywords: Artificial Intelligence, Honeybee, Agent-based system, Cognitive Computation.

Abstract: We describe the implementation if an agent-based controller for an autonomous robot with cognitive abilities

that reproduce homing capability in the foraging behaviour of the honeybee. The agent is based on a symbolic

representation of data and information and is written in a language designed to describe fine-grained large

scale parallelism, the Street language (Frost et al. 2015). The objective of this approach is to enable the direct

translation of agents written in Street into embedded hardware, to achieve compact, power efficient, autono-

mous cognitive processing capability.

1 INTRODUCTION

In the field of Artificial Intelligence research, the cog-

nitive architecture approach has shown considerable

promise in implementing autonomous cognitive

agents (Laird 2012). However, a limitation of these

systems is that they are constrained by the memory

capacity and computational performance of the gen-

eral-purpose computing platforms on which they run.

As cognitive agents become more complex and have

longer lifetimes, the number of matching conditions

that must be evaluated concurrently increases and, in

particular, the amount of long-term and working

memory required increases rapidly. Much design ef-

fort has been invested in moderating memory require-

ments in these systems. Our focus is on engineering

low power cognitive processors that can be embedded

in autonomous devices so we have been investigating

dedicated hardware systems that can implement cog-

nitive architectures.

Our developing architecture broadly follows a

production rule-based parallel processing method

(Frost et al. 2015; Numan et al. 2015). It uses a po-

tentially large array of dedicated production rule eval-

uation processors, which we call “productors”, to

concurrently match elements in a distributed memory

and to take actions by updating working memory.

The production rules and actions are expressed in a

customised language, loosely based on OPS-5 (Forgy

1981), which we call “Street”.

Currently, we are using a Java-based simulation

and debugging environment to develop agents in

Street, and we are concurrently pursuing agent devel-

opment and hardware-mapping as part of our research.

A key element of this research is verifying the capa-

bility of the Street language to capture useful levels

of cognitive behaviour, and understanding how re-

quirements for productors and working memory ca-

pacity scale up with agent complexity. We have iden-

tified the homing behaviour of honey bees as a suita-

ble test case for validating our approach.

It is known from neuroethological research (Men-

zel & Giurfa 2001), that cognitive behaviour requires

experience-dependent adaptation of neural networks,

and researchers believe that these procedures require

a more advanced and complex neuronal system than

has been discovered in the insect brain. However, ob-

servations and experiments have shown that the

honey bee (Apis mellifera) is an exception. (Menzel

2012). It is one of the most broadly researched euso-

cial insects in the field of animal ethology. Of partic-

ular interest is its ability to use magnetoreception

(Wajnberg et al. 2010) and landmark recognition (Fry

& Wehner 2002; Gillner, Weiß & Mallot 2008) with

memorization support for homing navigation after

long-distance foraging. (Menzel & Greggers 2015;

Menzel et al. 2005)

Honey bees have a capability of memorising land-

marks and magnetic field changes during a foraging

trip, (Fry & Wehner 2002; Labhart & Meyer 2002;

Menzel & Greggers 2015) and they use these memo-

ries as references to navigate back to their hive.

(Menzel & Greggers 2015) This behaviour requires

Yuan X., Liebelt M. and Phillips B.

A Cognitive Approach for Reproducing the Homing Behaviour of Honey Bees.

DOI: 10.5220/0006195705430550

In Proceedings of the 9th International Conference on Agents and Artiﬁcial Intelligence (ICAART 2017), pages 543-550

ISBN: 978-989-758-220-2

543

Figure 1: System Structure and Interaction.

memorization of characteristics of the experienced

landmarks, such as colours and sizes, (Labhart &

Meyer 2002) and magnetoreception (Wajnberg et al.

2010) at those memorised positions.

Randolf (Menzel 2012; Menzel & Giurfa 2001)

expressed the architecture of the honey bee’s brain as

a set of communicating modules. Modules are

associated with specific external stimuli. We have the

option of reproducing this modular functional organ-

isation by using separate, intercommunicating Street-

based agents and it is one of the objectives of this

work to understand whether the modular organisation

is effective and efficient when implemented in dedi-

cated hardware.

We propose to demonstrate that an efficient real-

time cognitive agent that is able to reproduce the

homing behaviour of the honeybee can be

implemented in compact electronic hardware, based

on our Street parallel production rule language. Thus,

we set an objective for the behaviour of our agent,

which requires it to reach a target and track its way

back to the initial position using its memorised land-

marks and magnetic data. Our hardware platform for

this work is a rover vehicle comprising a platform

with two drive wheels, a camera and magnetic sensor.

There are many advanced technologies inspired

by the behaviour of insects, and many of them have

made significant progress with applications. However,

our approach differs from prior work in that we are

using an architecture, loosely based on the cognitive

architecture of insects, which can be efficiently im-

plemented in electronic hardware and massively

scaled up in complexity.

2 THE AGENT-BASED SYSTEM

2.1 System Architecture

Our system reproduces the central decision-making

processes in honey bee homing behaviour by imitat-

ing its processing model in our agent-based system.

Our system has a structure that duplicates the mod-

ules in a bee’s brain (Menzel & Giurfa 2001), with

similar interactions between the modules. The system

structure and relationships between the modules are

shown in Figure 1. The system connects to input de-

vices that provide information about objects detected

within the visual field of the rover and an angle w.r.to

geographical north. Outputs are sent to a motion con-

troller, which receives mobility commands from the

Central Command Agent and drives the robot wheels

to produce the required movement. The visual object

processing module and the magnetic input processing

module both receive digital numeric inputs from the

sensors, but translate these into a symbolic represen-

tation. These two modules represent the sensory or-

gans of the bee, which sense external stimuli from the

real environment and transmit them into its nervous

system in the form of chemical elements and elec-

tronic impulses (Giurfa 2007; Kiya, Kunieda & Kubo

2007; Koch & Laurent 1999). The vision module

passes elements, which contain symbolic information

about objects detected in the field of view, to two dif-

ferent modules, which are both running our Street en-

gine simulator but loaded with different agents. One

memorises the information gathered in the foraging

process, and the other determines the movement ac-

tions to be made. This multi-agent structure allows

agent to develop their own Working Memory without

interaction or interference, which we hypothesize will

allow simpler implementation. The details of these

two agents are explained in sections 2.3 and 2.4.

2.2 Input Format

Our rule-based processing methods require data to be

stored in terms of symbolic elements comprising a tu-

ple of symbols, e.g. (

infoA infoB infoC). (Frost

et al. 2015) The number of symbols in an element is,

in principle, unlimited, but, in many cases, elements

capture single attributes of symbols or binary rela-

tionships between symbols and will therefore contain

two or three symbols. Symbols may take any form.

For clarity we assume that symbols may comprise any

printable characters, excluding white space. There-

fore, in the input processing modules, numeric inputs

are converted into symbolic representation using

fuzzy concepts. We restrict ourselves to processing

ICAART 2017 - 9th International Conference on Agents and Artiﬁcial Intelligence

544

Figure 2.

imprecise representations of information because bi-

ological creatures, to the best of our knowledge, only

recognise and process information in imprecise form.

More specifically speaking, bees are not able to meas-

ure an exact distance to a visualised object, and they

can only sense the surrounding magnetic field with

limited precision. We define a format for sensory in-

puts based on this characteristic, such that an example

of inputs describing a field of view in which there are

two objects appearing is the following.

<s> RED is_close

<s> BLUE is_far

<s> BLUE is_far_to RED

<s> blocksCount 2

Each line is an individual element, in which <s>

represents a unique reference ID for associating infor-

mation contained in multiple elements. The number

of elements is not fixed, and it depends on the number

of objects appearing in the same frame. For example,

if there are ݊ different coloured balls detected, there

will be ݊ elements content the distance information to

each ball and ܥ

ଶ

୬

elements describing the distance be-

tween each pair of balls. Also, the total number of the

detected objects in the field is counted. We refer to a

set of elements such as this, describing a single field

of view and with a common ID, as a Master Frame.

For the magnetic information, the format is as

(

<s> compass <angle>). However, this <angle>

number is only used as a numerical symbol for the

purposes of comparison between the current input and

recorded angles to trigger rotation if they do not

match. Section 2.4.4 introduces the details.

2.3 Memory Agent

This is an agent-based sub-system for information

storage. This module imitates the episodic memory

structure, which keeps a record of the bee’s experi-

ences, with some mechanism for tracing the order in

which they occurred.

The memory agent contains rules, which, in for-

aging mode, take input elements from the image pro-

cessing module and store these as a Master Frame.

Each Master Frame that contains information that is

recognised to be distinct from previous frames is

given a unique reference ID

<s>. These Master

Frames are linked up by the memory agent using an

element of the form

_ID1 followedBy _ID2, and

the reference ID of new inputs is stored in a new

element with the last reference ID, as

_IDnn fol-

lowedBy _IDNew. As a result, a series of elements

is constructed as a linked list of reference IDs in the

agent’s Working Memory.

Like the reaction when a honeybee starts to track

a route back to its hive, when the system switches to

homing mode, a command triggers the rules in the

A Cognitive Approach for Reproducing the Homing Behaviour of Honey Bees

545

Figure 3.

memory agent to output the last stored Master Frame,

which provides the set of elements describing the last

experienced episode to the Central Command Agent.

2.4 Central Command Agent

The Command Agent is the key controlling module

of the entire system. Firstly, this agent decides on

whether the system is in a free discovery foraging

mode or in homing mode. The difference, or the trig-

ger, between these two modes is whether a targeted

object has been reached. If the information element

representing the target has been received, the system

will be switched into the homing mode and will start

a route back to the original position.

This module receives information about detected

objects from the input processing module, and it both

transmits mode switching commands to the memory

module and receives target information inputs from it

while homing. In the homing mode, this module aims

to find a match between the target objects, using a

comparison process, as illustrated Figure 2. It at-

tempts to determine the relationship between its cur-

rent image frame and the next targeted Master Frame,

and to infer what kind of movement the robot should

make in order to make the two match. Based on the

information received from the input module, there are

multiple comparisons to be performed concurrently.

2.4.1 Distance to an Individual Object

Each image description in the memory agent contains

elements that describe, in fuzzy terms, the perceived

distance to each object in view. For example, the ele-

ment

_ID5 RED is_close,

represents that a red object was detected to be close.

While homing, if

_ID5 is the target Master Frame, the

agent compares the current visual input with it to as-

sess whether the same coloured object is being

viewed at the specified distance. In the command

agent, there are rules to react by moving to adjust the

distance. If the target recorded is closer than an object

currently visualised, the agent instructs the motion

command module to move closer the object. Con-

versely, the agent outputs fall back instructions if the

object is closer than the distance in the target Master

Frame.

2.4.2 Distance between Objects

If more than one object is detected in the visual field,

the visual processing module also generates infor-

mation elements about the relative distance between

the objects. Therefore, a distance between two objects

is also a piece of information that can be used to trig-

ger a movement command. For example, if the inputs

about the contain the element

<new> BLUE is_far_to RED

and the next image is sequence recorded in the

memory has

_ID6 BLUE is_close_to RED

then, the agent will instruct the motion module to

move in a direction that is closer to the two objects,

to increase the perceived separation.

2.4.3 Number of Objects

Another index in a comparison is the number of ob-

jects detected. This is a significantly important fea-

ture for assessing the current position relative to a pre-

vious record. In order to assess either the current

position matches that indicated by the target Master

Frame, the central command agent counts the number

of matched objects to ensure all objtcs in the targeted

Master Frame are. In our architecture, production

rules relating to distances distances are matched indi-

vidually and concurrently, and it is necessary to also

ICAART 2017 - 9th International Conference on Agents and Artiﬁcial Intelligence

546

Figure 4.

match the total number of objects in frmae to avoid

any false positives.

2.4.4 Compass Reference

It is known that honey bees possess a sensory system

that is able to detect and remember the direction in

which they are heading. In flight, they can yaw to

match up their current heading with a remembered di-

rection.

Our system includes a magnetic compass that is

intended to reproduce this feature of honeybees. We

use the number output by the compass (the angle

w.r.to geographical North) as a symbolic indicator of

direction, but do not perform any arithmetic pro-

cessing on this number.

2.5 Motion

The motion activities depend on the mode of the sys-

tem. While the system is in foraging mode, the motion

command module generates random movement in-

structions to have a free exploration of the environ-

ment to discover the target object. When the system

is in homing mode, all motion commands will be

based on instructions from the central command agent.

Clearly, on a two wheeled robotic platform move-

ment is confined to planar surfaces and we do not at-

tempt to model the three dimensional freedom of

movement that flying insects have. Therefore, move-

ments only simulate three types of mobility actions,

which are linear movements in two orthogonal direc-

tions and yaw rotations.

3 EXPERIMENT ENVIRONMENT

We were inspired by ethological experimental tech-

niques (Chittka & Tautz 2003; Giurfa 2003; GIURFA

et al. 1999; Horridge 2006; Srinivasan, Zhang &

Lehrer 1998), which typically use shapes and colour

symbols in an artificial environment to discover and

verify the ability of honey bees to memorise route in-

formation. Therefore, we build a physical environ-

ment with a pure colour background and coloured

plastic balls to represent landmarks, and, with the in-

tention of real-life autonomous system application, a

small two-wheeled robot serving as the experiment-

ing artificial bee. Our target object is a simple yellow

ball.

Our Java honeybee system runs on a single-board

computer (SBC), the LattePanda. The LattePanda

runs the Windows 10 operating system, which we

A Cognitive Approach for Reproducing the Homing Behaviour of Honey Bees

547

have chosen for ease of portability of our Street

simulation environment. Ultimately the Street simu-

lation on the Windows platform will be replaced by a

dedicated Street engine implemented in custom hard-

ware.

Motion commands are transmitted to an on-board

Arduino microcomputer, which controls two stepper

motors through two motor-controllers, the A4899. A

Pixy Cam5, which is a camera with in-built detection

of coloured objects, is used as the visual sensor. It

provides position, colour and size information about

any objects detected. An LSM303 compass module

provides information about the magnetic field. The

device includes both an accelerometer and a magne-

tometer, but we don’t use the accelerometer. Both in-

put devices connect to a Micro Arduino Board, and

inputs are provided to the LattePanda through a serial

communication port.

During our experiments, the Honeybee System il-

lustrated in Figure 1, including the Street simulators

running the two agents, runs in a Java environment on

the LattePanda SBC.

The experiment process starts with placing the

honeybee simulating rover at an initial position, rep-

resenting a hive, on our testing ground. Then, the

rover starts with its foraging phase to find the target

object, and as it does so the system records any land-

mark changes. Once the rover reaches the target, it

starts the homing phase based on the experienced

pathway to track back to the original position.

4 SYSTEM BEHAVIOUR

4.1 Foraging

In this phase, the mobility module can generate any

uncoordinated motion instructions, which allow the

rover to explore the surrounding environment until

the target object is located serendipitously. During

this phase of free discovery of the environment, a list

of any objects encountered are recorded in sequence

in the episodic memory of the Memory Agent, in the

previously described.

Figure 3 shows an example of as sequence of

Master Frames after a series of movements. The rover

starts at a position, which is right next to a red ball,

and then it reverses. On its way, two Master Frames

are recorded. MF

_ID2 records that the rover is

close to the red object, and _ID3 records that the

rover is

far from the red ball. Our rover has only cap-

tured an image of a red ball so far, and each set of

these stored elements are triggered by the change of

perceived distance to that red object. Then, the rover

rotates clockwise, and a new Master Frame,

_ID4,

records that two coloured blocks are viewed at the

same time. Both of the objects are at a

far distance

from the rover, and the relative distance between

them is

far. After that, the rover moves forwards,

and Master Frame

_ID5 is created when the rover

sees only the green object, with a

far distance.

A new Master Frame is created only if any visual

information changes, such as changes of perceived

distances and the number of detected objects. Putting

it another way, the system is able to recognise

changes in image properties during the movement,

and it only creates a new Master Record when the

situation changes.

When new elements are generated for a new Mas-

ter Frame in the Working Memory of the Memory

Agent and only when that it happening, an element

containing magnetic information is also created. As

the example in Figure 3 shows, the compass angles

with

_ID4 and _ID5 are different because (_ID4

compass 237) is created at the moment when the

green ball just gets into the frame, and

(_ID5 com-

pass 248)

is recorded when the edge of the red ball

cannot be viewed any more.

4.2 Homing

4.2.1 Homing Strategy

This homing activity is designed to perform in much

the same way as it has been observed to happen in the

ethological experiments. This is to say, our rover is

expected to track back to the starting point, the hive,

by revisiting each recorded Master Frame, the land-

marks, one after another.

Our homing mode is triggered after the target ob-

ject is reached. The first step is to request the last

Master Frame from the memory module as the first

target. Then, the command agent compares this

information with the current visual inputs. If any

elements does not match, the command module

generates motion activities to resolve any differs in

distance.

Actions and instructions are generated as

described in Figure 4. For example, in some cases, the

rover does not detect in its current visual field any ob-

jects of the target Master Frame. In this case, the rover

rotates to match its magnetic orientation to that rec-

orded for the target Master Frame, and then generates

commands to adjust the dirtance, once objects in the

target MF are visualised. If no targeted objects can be

detected even after matching its magnetic orientation

to the MF, the rover continues to rotate. If it still does

not find the target objects after a full circle of rotation,

ICAART 2017 - 9th International Conference on Agents and Artiﬁcial Intelligence

548

Figure 5.

the agent requests the rover to reverse to gain a

broader view. In some circumstances, the agent

instructs multiple movements, such as both

longitudinal and transversal movements, when more

than two elements are required to be changed in the

current situation. After moving to a position such that

the number of matched objects is same as the number

in the target Master Frame, the command agent deter-

mines that this targeted Master Frame has been

reached and requests the next one from the memory

module. This process repeats until the memory agent

detects that there are no more stored Master Frames,

which means that the original position has been

reached.

4.2.2 Test Method

In order to test homing behaviour, we use a manual

foraging mode, in which we manually move the rover

along a planned route in order to produce a predicta-

ble homing pathway. Once the rover has completed

the planned route, we command the system to enter

the homing mode and start to observe the rover’s

homing performance.

Our initial test has been with single object in the

field of view but we have also tested multiple object

views with linear movements only. We have found

that if objects are at different distances from the rover

the homing navigation movements are more accurate.

This is because there are more references for dis-

tances along the same line.

In the next stage of testing, we allow the rover to

rotate. With this additional freedom of mobility, the

rover can resolve any magnetic orientation differ-

ences recorded between its current position and the

target Master Frame, to find objects that are not in the

current field of view.

4.2.3 Example: Linear Movements with Ro-

tations

To give an illustration, Figure 5 shows an example of

the process. We manually move the rover through a

path such that there are six Master Frames captured

and stacked. While the rover tracks back to the origi-

nal position, it refers to each Master Frame one after

another.

Firstly, the Memory Agent outputs all of the ele-

ments of the last Master Frame, as the first target, to

the Command Agent. It immediately matches up that

the characteristics of the target MF with its current

view, which is a

nextTo distance with a green ball

and a same magnetic angle at the end of. The Com-

mand Agent determines t has been found and requests

the next target MF. This causes the Command Agent

to direct to the rover to reverse to make the distance

to the green object

Close. After that, the Command

Agent receives the third recorded Master Frame, and

the agent directs the rover to perform a reverse right

turn to achieve a far view of the green ball with a dif-

ferent magnetically-determined orientation. This ac-

tion occurs because, when we manually rotate the

rover during the foraging stage, the system records a

new Master Frame at the moment that the green ball

just comes into the field of view, and it is not facing

directly to the green ball yet. Therefore, on the way to

reach this target position, a combined movement is

performed to justify the magnetic orientation and in-

crease in the linear distance to the green ball. The next

target MF contains (

_ID3 RED is_far) and another

change in magnetic orientation, such that the rover

faces the initial direction. Because it can’t initially see

the red ball the rover rotates towards the targeted

orientation. During this anti-clockwise rotation, the

red ball first appears in frame with a

far distance, so

no motion is required to adjust the distance. The next

MF indicates that the red ball should be

close so the

Command Agent instructs the rover to move forward.

It stops moving at the moment when the final Master

Frame is matched and the Memory Agent indicates

A Cognitive Approach for Reproducing the Homing Behaviour of Honey Bees

549

that that this is the end of the route. This homing tra-

jectory route is shown in the lower part of Figure 5.

5 CONCLUSIONS

Our agent-based system shows behaviour with simi-

lar characteristics to the homing behaviour of bees,

and it serves as a proof of concept for our hardware-

based cognitive agent approach. Our system structure

shows the feasibility of a hardware efficient system

that is able to reproduce the cognitive behaviour of a

simple biological system.

This research is extendable in terms of the density

and complexity of the behaviour we reproduce. In-

sects, like honey bees, use many intelligent features,

which have been studied and explained in some detail,

such as the random foraging activity of ants

(Traniello 1989). Our future work will involve mod-

elling more advanced cognitive behaviours of crea-

tures such as the ant, and developing the technology

for implementing Street agents directly in low-power

hardware.

In the longer term we aim to demonstrate that with

the very high speed processing and high levels of

parallelism available in custom microelectronic

hardware, it will possible to use a Street-based

architecture to implement significantly higher levels

of cognitive behaviour than insect foraging.

REFERENCES

Chittka, L & Tautz, J 2003, 'The spectral input to honeybee

visual odometry', Journal of Experimental Biology, vol.

206, no. 14, pp. 2393-2397.

Forgy, CL 1981, OPS5 user's manual, DTIC Document.

Frost, J, Numan, MW, Liebelt, M & Phillips, BJ 2015, 'A

new computer for cognitive computing', Cognitive

Informatics & Cognitive Computing (ICCI* CC), 2015

IEEE 14th International Conference on, IEEE, pp. 33-

38.

Fry, SN & Wehner, R 2002, 'Honey bees store landmarks

in an egocentric frame of reference', Journal of

Comparative Physiology A, vol. 187, no. 12, pp. 1009-

1016.

Gillner, S, Weiß, AM & Mallot, HA 2008, 'Visual homing

in the absence of feature-based landmark information',

Cognition, vol. 109, no. 1, pp. 105-122.

Giurfa, M 2003, 'Cognitive neuroethology: dissecting non-

elemental learning in a honeybee brain', Current

opinion in neurobiology, vol. 13, no. 6, pp. 726-735.

Giurfa, M 2007, 'Behavioral and neural analysis of

associative learning in the honeybee: a taste from the

magic well', Journal of Comparative Physiology A, vol.

193, no. 8, pp. 801-824.

GIURFA, M, HAMMER, M, STACH, S, STOLLHOFF, N,

MÜLLER-DEISIG, N & MIZYRYCKI, C 1999,

'Pattern learning by honeybees: conditioning procedure

and recognition strategy', Animal behaviour, vol. 57, no.

2, pp. 315-324.

Horridge, A 2006, 'Some labels that are recognized on

landmarks by the honeybee (Apis mellifera)', Journal

of insect physiology, vol. 52, no. 11, pp. 1254-1271.

Kiya, T, Kunieda, T & Kubo, T 2007, 'Increased neural

activity of a mushroom body neuron subtype in the

brains of forager honeybees', PLoS One, vol. 2, no. 4, p.

e371.

Koch, C & Laurent, G 1999, 'Complexity and the nervous

system', Science, vol. 284, no. 5411, pp. 96-98.

Labhart, T & Meyer, EP 2002, 'Neural mechanisms in

insect navigation: polarization compass and odometer',

Current opinion in neurobiology, vol. 12, no. 6, pp.

707-714.

Laird, JE 2012, The Soar cognitive architecture, MIT Press.

Menzel, R 2012, 'The honeybee as a model for

understanding the basis of cognition', Nature Reviews

Neuroscience, vol. 13, no. 11, pp. 758-768.

Menzel, R & Giurfa, M 2001, 'Cognitive architecture of a

mini-brain: the honeybee', Trends in cognitive sciences,

vol. 5, no. 2, pp. 62-71.

Menzel, R & Greggers, U 2015, 'The memory structure of

navigation in honeybees', Journal of Comparative

Physiology A, vol. 201, no. 6, pp. 547-561.

Menzel, R, Greggers, U, Smith, A, Berger, S, Brandt, R,

Brunke, S, Bundrock, G, Hülse, S, Plümpe, T &

Schaupp, F 2005, 'Honey bees navigate according to a

map-like spatial memory', Proceedings of the National

Academy of Sciences of the United States of America,

vol. 102, no. 8, pp. 3040-3045.

Numan, MW, Frost, J, Phillips, BJ & Liebelt, M 2015, 'A

Network-based Communication Platform for a

Cognitive Computer',

AIC 2015, p. 94.

Srinivasan, M, Zhang, S & Lehrer, M 1998, 'Honeybee

navigation: odometry with monocular input', Animal

behaviour, vol. 56, no. 5, pp. 1245-1259.

Traniello, JF 1989, 'Foraging strategies of ants', Annual

review of entomology, vol. 34, no. 1, pp. 191-210.

Wajnberg, E, Acosta-Avalos, D, Alves, OC, de Oliveira, JF,

Srygley, RB & Esquivel, DM 2010, 'Magnetoreception

in eusocial insects: an update', Journal of the Royal

Society Interface, vol. 7, no. Suppl 2, pp. S207-S225.

ICAART 2017 - 9th International Conference on Agents and Artiﬁcial Intelligence

550